import re import torch import importlib import numpy as np from collections import Counter from TTS.utils.generic_utils import check_argument def split_dataset(items): speakers = [item[-1] for item in items] is_multi_speaker = len(set(speakers)) > 1 eval_split_size = min(500, int(len(items) * 0.01)) assert eval_split_size > 0, " [!] You do not have enough samples to train. You need at least 100 samples." np.random.seed(0) np.random.shuffle(items) if is_multi_speaker: items_eval = [] speakers = [item[-1] for item in items] speaker_counter = Counter(speakers) while len(items_eval) < eval_split_size: item_idx = np.random.randint(0, len(items)) speaker_to_be_removed = items[item_idx][-1] if speaker_counter[speaker_to_be_removed] > 1: items_eval.append(items[item_idx]) speaker_counter[speaker_to_be_removed] -= 1 del items[item_idx] return items_eval, items return items[:eval_split_size], items[eval_split_size:] # from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1 def sequence_mask(sequence_length, max_len=None): if max_len is None: max_len = sequence_length.data.max() seq_range = torch.arange(max_len, dtype=sequence_length.dtype, device=sequence_length.device) # B x T_max return seq_range.unsqueeze(0) < sequence_length.unsqueeze(1) def to_camel(text): text = text.capitalize() return re.sub(r'(?!^)_([a-zA-Z])', lambda m: m.group(1).upper(), text) def setup_model(num_chars, num_speakers, c, speaker_embedding_dim=None): print(" > Using model: {}".format(c.model)) MyModel = importlib.import_module('TTS.tts.models.' + c.model.lower()) MyModel = getattr(MyModel, to_camel(c.model)) if c.model.lower() in "tacotron": model = MyModel(num_chars=num_chars + getattr(c, "add_blank", False), num_speakers=num_speakers, r=c.r, postnet_output_dim=int(c.audio['fft_size'] / 2 + 1), decoder_output_dim=c.audio['num_mels'], gst=c.use_gst, gst_embedding_dim=c.gst['gst_embedding_dim'], gst_num_heads=c.gst['gst_num_heads'], gst_style_tokens=c.gst['gst_style_tokens'], gst_use_speaker_embedding=c.gst['gst_use_speaker_embedding'], memory_size=c.memory_size, attn_type=c.attention_type, attn_win=c.windowing, attn_norm=c.attention_norm, prenet_type=c.prenet_type, prenet_dropout=c.prenet_dropout, forward_attn=c.use_forward_attn, trans_agent=c.transition_agent, forward_attn_mask=c.forward_attn_mask, location_attn=c.location_attn, attn_K=c.attention_heads, separate_stopnet=c.separate_stopnet, bidirectional_decoder=c.bidirectional_decoder, double_decoder_consistency=c.double_decoder_consistency, ddc_r=c.ddc_r, speaker_embedding_dim=speaker_embedding_dim) elif c.model.lower() == "tacotron2": model = MyModel(num_chars=num_chars + getattr(c, "add_blank", False), num_speakers=num_speakers, r=c.r, postnet_output_dim=c.audio['num_mels'], decoder_output_dim=c.audio['num_mels'], gst=c.use_gst, gst_embedding_dim=c.gst['gst_embedding_dim'], gst_num_heads=c.gst['gst_num_heads'], gst_style_tokens=c.gst['gst_style_tokens'], gst_use_speaker_embedding=c.gst['gst_use_speaker_embedding'], attn_type=c.attention_type, attn_win=c.windowing, attn_norm=c.attention_norm, prenet_type=c.prenet_type, prenet_dropout=c.prenet_dropout, forward_attn=c.use_forward_attn, trans_agent=c.transition_agent, forward_attn_mask=c.forward_attn_mask, location_attn=c.location_attn, attn_K=c.attention_heads, separate_stopnet=c.separate_stopnet, bidirectional_decoder=c.bidirectional_decoder, double_decoder_consistency=c.double_decoder_consistency, ddc_r=c.ddc_r, speaker_embedding_dim=speaker_embedding_dim) elif c.model.lower() == "glow_tts": model = MyModel(num_chars=num_chars + getattr(c, "add_blank", False), hidden_channels_enc=c['hidden_channels_encoder'], hidden_channels_dec=c['hidden_channels_decoder'], hidden_channels_dp=c['hidden_channels_duration_predictor'], out_channels=c.audio['num_mels'], encoder_type=c.encoder_type, encoder_params=c.encoder_params, use_encoder_prenet=c["use_encoder_prenet"], num_flow_blocks_dec=12, kernel_size_dec=5, dilation_rate=1, num_block_layers=4, dropout_p_dec=0.05, num_speakers=num_speakers, c_in_channels=0, num_splits=4, num_squeeze=2, sigmoid_scale=False, mean_only=True, external_speaker_embedding_dim=speaker_embedding_dim) elif c.model.lower() == "speedy_speech": model = MyModel(num_chars=num_chars + getattr(c, "add_blank", False), out_channels=c.audio['num_mels'], hidden_channels=c['hidden_channels'], positional_encoding=c['positional_encoding'], encoder_type=c['encoder_type'], encoder_params=c['encoder_params'], decoder_type=c['decoder_type'], decoder_params=c['decoder_params'], c_in_channels=0) return model def is_tacotron(c): return False if c['model'] in ['speedy_speech', 'glow_tts'] else True def check_config_tts(c): check_argument('model', c, enum_list=['tacotron', 'tacotron2', 'glow_tts', 'speedy_speech'], restricted=True, val_type=str) check_argument('run_name', c, restricted=True, val_type=str) check_argument('run_description', c, val_type=str) # AUDIO check_argument('audio', c, restricted=True, val_type=dict) # audio processing parameters check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056) check_argument('fft_size', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058) check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000) check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000, alternative='win_length') check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000, alternative='hop_length') check_argument('preemphasis', c['audio'], restricted=True, val_type=float, min_val=0, max_val=1) check_argument('min_level_db', c['audio'], restricted=True, val_type=int, min_val=-1000, max_val=10) check_argument('ref_level_db', c['audio'], restricted=True, val_type=int, min_val=0, max_val=1000) check_argument('power', c['audio'], restricted=True, val_type=float, min_val=1, max_val=5) check_argument('griffin_lim_iters', c['audio'], restricted=True, val_type=int, min_val=10, max_val=1000) # vocabulary parameters check_argument('characters', c, restricted=False, val_type=dict) check_argument('pad', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str) check_argument('eos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str) check_argument('bos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str) check_argument('characters', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str) check_argument('phonemes', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str) check_argument('punctuations', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str) # normalization parameters check_argument('signal_norm', c['audio'], restricted=True, val_type=bool) check_argument('symmetric_norm', c['audio'], restricted=True, val_type=bool) check_argument('max_norm', c['audio'], restricted=True, val_type=float, min_val=0.1, max_val=1000) check_argument('clip_norm', c['audio'], restricted=True, val_type=bool) check_argument('mel_fmin', c['audio'], restricted=True, val_type=float, min_val=0.0, max_val=1000) check_argument('mel_fmax', c['audio'], restricted=True, val_type=float, min_val=500.0) check_argument('spec_gain', c['audio'], restricted=True, val_type=[int, float], min_val=1, max_val=100) check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool) check_argument('trim_db', c['audio'], restricted=True, val_type=int) # training parameters check_argument('batch_size', c, restricted=True, val_type=int, min_val=1) check_argument('eval_batch_size', c, restricted=True, val_type=int, min_val=1) check_argument('r', c, restricted=True, val_type=int, min_val=1) check_argument('gradual_training', c, restricted=False, val_type=list) check_argument('mixed_precision', c, restricted=False, val_type=bool) # check_argument('grad_accum', c, restricted=True, val_type=int, min_val=1, max_val=100) # loss parameters check_argument('loss_masking', c, restricted=True, val_type=bool) if c['model'].lower() in ['tacotron', 'tacotron2']: check_argument('decoder_loss_alpha', c, restricted=True, val_type=float, min_val=0) check_argument('postnet_loss_alpha', c, restricted=True, val_type=float, min_val=0) check_argument('postnet_diff_spec_alpha', c, restricted=True, val_type=float, min_val=0) check_argument('decoder_diff_spec_alpha', c, restricted=True, val_type=float, min_val=0) check_argument('decoder_ssim_alpha', c, restricted=True, val_type=float, min_val=0) check_argument('postnet_ssim_alpha', c, restricted=True, val_type=float, min_val=0) check_argument('ga_alpha', c, restricted=True, val_type=float, min_val=0) if c['model'].lower == "speedy_speech": check_argument('ssim_alpha', c, restricted=True, val_type=float, min_val=0) check_argument('l1_alpha', c, restricted=True, val_type=float, min_val=0) check_argument('huber_alpha', c, restricted=True, val_type=float, min_val=0) # validation parameters check_argument('run_eval', c, restricted=True, val_type=bool) check_argument('test_delay_epochs', c, restricted=True, val_type=int, min_val=0) check_argument('test_sentences_file', c, restricted=False, val_type=str) # optimizer check_argument('noam_schedule', c, restricted=False, val_type=bool) check_argument('grad_clip', c, restricted=True, val_type=float, min_val=0.0) check_argument('epochs', c, restricted=True, val_type=int, min_val=1) check_argument('lr', c, restricted=True, val_type=float, min_val=0) check_argument('wd', c, restricted=is_tacotron(c), val_type=float, min_val=0) check_argument('warmup_steps', c, restricted=True, val_type=int, min_val=0) check_argument('seq_len_norm', c, restricted=is_tacotron(c), val_type=bool) # tacotron prenet check_argument('memory_size', c, restricted=is_tacotron(c), val_type=int, min_val=-1) check_argument('prenet_type', c, restricted=is_tacotron(c), val_type=str, enum_list=['original', 'bn']) check_argument('prenet_dropout', c, restricted=is_tacotron(c), val_type=bool) # attention check_argument('attention_type', c, restricted=is_tacotron(c), val_type=str, enum_list=['graves', 'original', 'dynamic_convolution']) check_argument('attention_heads', c, restricted=is_tacotron(c), val_type=int) check_argument('attention_norm', c, restricted=is_tacotron(c), val_type=str, enum_list=['sigmoid', 'softmax']) check_argument('windowing', c, restricted=is_tacotron(c), val_type=bool) check_argument('use_forward_attn', c, restricted=is_tacotron(c), val_type=bool) check_argument('forward_attn_mask', c, restricted=is_tacotron(c), val_type=bool) check_argument('transition_agent', c, restricted=is_tacotron(c), val_type=bool) check_argument('transition_agent', c, restricted=is_tacotron(c), val_type=bool) check_argument('location_attn', c, restricted=is_tacotron(c), val_type=bool) check_argument('bidirectional_decoder', c, restricted=is_tacotron(c), val_type=bool) check_argument('double_decoder_consistency', c, restricted=is_tacotron(c), val_type=bool) check_argument('ddc_r', c, restricted='double_decoder_consistency' in c.keys(), min_val=1, max_val=7, val_type=int) if c['model'].lower() in ['tacotron', 'tacotron2']: # stopnet check_argument('stopnet', c, restricted=is_tacotron(c), val_type=bool) check_argument('separate_stopnet', c, restricted=is_tacotron(c), val_type=bool) # Model Parameters for non-tacotron models if c['model'].lower == "speedy_speech": check_argument('positional_encoding', c, restricted=True, val_type=type) check_argument('encoder_type', c, restricted=True, val_type=str) check_argument('encoder_params', c, restricted=True, val_type=dict) check_argument('decoder_residual_conv_bn_params', c, restricted=True, val_type=dict) # GlowTTS parameters check_argument('encoder_type', c, restricted=not is_tacotron(c), val_type=str) # tensorboard check_argument('print_step', c, restricted=True, val_type=int, min_val=1) check_argument('tb_plot_step', c, restricted=True, val_type=int, min_val=1) check_argument('save_step', c, restricted=True, val_type=int, min_val=1) check_argument('checkpoint', c, restricted=True, val_type=bool) check_argument('tb_model_param_stats', c, restricted=True, val_type=bool) # dataloading # pylint: disable=import-outside-toplevel from TTS.tts.utils.text import cleaners check_argument('text_cleaner', c, restricted=True, val_type=str, enum_list=dir(cleaners)) check_argument('enable_eos_bos_chars', c, restricted=True, val_type=bool) check_argument('num_loader_workers', c, restricted=True, val_type=int, min_val=0) check_argument('num_val_loader_workers', c, restricted=True, val_type=int, min_val=0) check_argument('batch_group_size', c, restricted=True, val_type=int, min_val=0) check_argument('min_seq_len', c, restricted=True, val_type=int, min_val=0) check_argument('max_seq_len', c, restricted=True, val_type=int, min_val=10) check_argument('compute_input_seq_cache', c, restricted=True, val_type=bool) # paths check_argument('output_path', c, restricted=True, val_type=str) # multi-speaker and gst check_argument('use_speaker_embedding', c, restricted=True, val_type=bool) check_argument('use_external_speaker_embedding_file', c, restricted=c['use_speaker_embedding'], val_type=bool) check_argument('external_speaker_embedding_file', c, restricted=c['use_external_speaker_embedding_file'], val_type=str) if c['model'].lower() in ['tacotron', 'tacotron2'] and c['use_gst']: check_argument('use_gst', c, restricted=is_tacotron(c), val_type=bool) check_argument('gst', c, restricted=is_tacotron(c), val_type=dict) check_argument('gst_style_input', c['gst'], restricted=is_tacotron(c), val_type=[str, dict]) check_argument('gst_embedding_dim', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=0, max_val=1000) check_argument('gst_use_speaker_embedding', c['gst'], restricted=is_tacotron(c), val_type=bool) check_argument('gst_num_heads', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=2, max_val=10) check_argument('gst_style_tokens', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=1, max_val=1000) # datasets - checking only the first entry check_argument('datasets', c, restricted=True, val_type=list) for dataset_entry in c['datasets']: check_argument('name', dataset_entry, restricted=True, val_type=str) check_argument('path', dataset_entry, restricted=True, val_type=str) check_argument('meta_file_train', dataset_entry, restricted=True, val_type=[str, list]) check_argument('meta_file_val', dataset_entry, restricted=True, val_type=str)